ML Systems Engineer, Data Labeling Engineering - Early Career

GM•Sunnyvale, CA

18h•Hybrid

About The Position

Help teach our self-driving vehicles how to see and understand the world. The Data Labeling Engineering team designs, builds, and operates hybrid human/machine data labeling tools and pipelines that power autonomous vehicle machine learning models within General Motors' AV organization. We operate in the intersection of software engineering, data engineering, and AI/ML, defining the strategies, tooling, and quality controls that create reliable training data at scale. Our tools and platform are used by thousands of users and consumers. We own a modern full-stack architecture including TypeScript/React, Python, GraphQL, Golang, and ML model services, which powers data-annotation pipelines and machine-led training data solutions at foundation-model scale. We partner closely across AI/ML engineers, Product Operations, Product Management, Data Science, and other ML Platform groups. As an early-career Software Engineer on the Data Labeling Engineering team, you will build tools and services that help machine learning teams create high-quality training data for autonomous driving. Your work may span frontend experiences, backend services, data pipelines, machine learning integrations, and quality systems used by labelers, ML engineers, and operations teams. This role is designed for a recent college graduate or engineer early in their career who wants to own meaningful pieces of a platform, grow their technical expertise, and work directly on systems that enable the next generation of AV capabilities. You will learn from experienced engineers while contributing to production systems and developing depth across frontend, backend, data, and ML-adjacent technologies.

Requirements

Recently completed a bachelor’s, master’s, or PhD degree in Computer Science, Computer Engineering, Software Engineering, Artificial Intelligence, Machine Learning, or a related STEM field. For completed degrees, graduation must have occurred within the past 12 months.
Experience shipping software or features through internships, research, academic projects, or prior professional work.
Programming experience in one or more languages such as Python, TypeScript, JavaScript, Go, Java, or C++.
Familiarity with software fundamentals, including object-oriented design, design patterns, data structures, algorithms, API/interface design, and engineering best practices.
Strong communication and collaboration skills; you can explain tradeoffs, influence peers, and work through ambiguity with cross-functional partners.
Interest in autonomous vehicles, robotics, machine learning, data-centric AI, or developer and ML platform technologies.

Nice To Haves

Degree completed between May 2025 and August 2026, with availability to begin employment in 2026.
Hands-on experience leveraging AI tools (agentic workflows, knowledge acquisition, documentation generation, operational triage, etc.) to accelerate understanding, implementation, debugging, and delivery of new capabilities.
Proficiency in writing and reviewing high-quality, scalable, and performant full-stack code using technologies and languages like Python, TypeScript, Go, React, SQL, Redux, gRPC, GraphQL, WebGL, etc.
Solid understanding of scalable software system design including data modeling and API/interface design.
Strong fundamentals in object-oriented design and design patterns, data structures, algorithms, and engineering best practices (TDD, code quality, observability, CI/CD).
Driven to learn new technologies and deepen your expertise across frontend, backend, and data/ML-adjacent systems.
Empathetic to user challenges (from labelers to ML engineers to Ops) and excited to turn messy workflows into simple, intuitive tools.

Responsibilities

Level up how ML teams work with data: Develop automation and tooling that give ML engineers deep insight into labeling workflows and data quality (e.g., efficiency dashboards, auto-QA, autolabel review tools), reducing iteration time from idea to trained model.
Apply ML to labeling itself: Collaborate with ML engineers to design and integrate ML-driven data annotation (pre-labeling, autolabeling, active learning loops), helping us move from human-only to machine-led labeling at scale.
Build high-impact labeling experiences: Design, implement, and test scalable, high-performance user experiences and services using modern full-stack and/or frontend technologies. You’ll ship features spanning multiple surface-areas that directly affect how quickly and accurately we can label data for new models and cities.
Champion AI-assisted engineering: Use and advocate for modern AI-powered development workflows (code assistants, automated documentation, test generation, etc.) to increase build-velocity while maintaining code and product quality.